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Active Learning Strategies for Weakly-Supervised Object Detection

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13690))

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Abstract

Object detectors trained with weak annotations are affordable alternatives to fully-supervised counterparts. However, there is still a significant performance gap between them. We propose to narrow this gap by fine-tuning a base pre-trained weakly-supervised detector with a few fully-annotated samples automatically selected from the training set using “box-in-box” (BiB), a novel active learning strategy designed specifically to address the well-documented failure modes of weakly-supervised detectors. Experiments on the VOC07 and COCO benchmarks show that BiB outperforms other active learning techniques and significantly improves the base weakly-supervised detector’s performance with only a few fully-annotated images per class. BiB reaches 97% of the performance of fully-supervised Fast RCNN with only 10% of fully-annotated images on VOC07. On COCO, using on average 10 fully-annotated images per class, or equivalently 1% of the training set, BiB also reduces the performance gap (in AP) between the weakly-supervised detector and the fully-supervised Fast RCNN by over 70%, showing a good trade-off between performance and data efficiency. Our code is publicly available at https://github.com/huyvvo/BiB.

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Notes

  1. 1.

    In case of a draw, an image is randomly selected.

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Acknowledgements

This work was supported in part by the Inria/NYU collaboration, the Louis Vuitton/ENS chair on artificial intelligence and the French government under management of Agence Nationale de la Recherche as part of the “Investissements d’avenir” program, reference ANR19-P3IA-0001 (PRAIRIE 3IA Institute). It was performed using HPC resources from GENCI-IDRIS (Grant 2021-AD011013055). Huy V. Vo was supported in part by a Valeo/Prairie CIFRE PhD Fellowship.

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Authors and Affiliations

  1. Inria and DI/ENS (ENS-PSL, CNRS, Inria), Paris, France

    Huy V. Vo & Jean Ponce

  2. Valeo.ai, Paris, France

    Huy V. Vo, Oriane Siméoni, Spyros Gidaris, Andrei Bursuc & Patrick Pérez

  3. Center for Data Science, New York University, New York, USA

    Jean Ponce

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  1. Huy V. Vo
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  2. Oriane Siméoni
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Correspondence to Huy V. Vo .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Vo, H.V., Siméoni, O., Gidaris, S., Bursuc, A., Pérez, P., Ponce, J. (2022). Active Learning Strategies for Weakly-Supervised Object Detection. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13690. Springer, Cham. https://doi.org/10.1007/978-3-031-20056-4_13

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